Providing stability maps for milling operations

Lan, Jean-Vincent Le; Marty, Audrey; Debongnie, Jean-François

doi:10.1016/j.ijmachtools.2006.09.026

Cited by 17 publications

(10 citation statements)

References 8 publications

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“…The SLD data is derived from machine tools in their downtime. As a result, there are factors that cannot be accounted for in the analysis, such as the rigidity of the spindle system, the effect of excessive heat of machine tool parts in operation and other workpiece-related factors (e.g., mass-related damping properties of the workpiece, material defects or the variable diameter of cut) [40,42]. It is important that the obtained SLD results be cross-referenced with the cutting data from dynamic milling tests.…”

Section: Introduction—state-of-the-artmentioning

confidence: 99%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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This paper set out to investigate the effect of cutting speed vc and trochoidal step str modification on selected machinability parameters (the cutting force components and vibration). In addition, for a more detailed analysis, selected surface roughness parameters were investigated. The research was carried out for two grades of magnesium alloys—AZ91D and AZ31—and aimed to determine stable machining parameters and to investigate the dynamics of the milling process, i.e., the resulting change in the cutting force components and in vibration. The tests were performed for the specified range of cutting parameters: vc = 400–1200 m/min and str = 5–30%. The results demonstrate a significant effect of cutting data modification on the parameter under scrutiny—the increase in vc resulted in the reduction of the cutting force components and the displacement and level of vibration recorded in tests. Selected cutting parameters were modelled by means of Statistica Artificial Neural Networks (Radial Basis Function and Multilayered Perceptron), which, furthermore, confirmed the suitability of neural networks as a tool for prediction of the cutting force and vibration in milling of magnesium alloys.

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Section: Introduction—state-of-the-artmentioning

confidence: 99%

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

et al. 2019

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“…The second is related to the displacement of the tool over the modes of vibration of the part, e.g., when the tool is machining over a nodal point of a mode, that mode is negligible [18]. The third reason is the variation of the modal parameters in the cutting area [19][20][21][22]. Depending on the size of the tool and the frequency of the modes that are being excited, this variation means that the dynamic response to each cutting edge impact is different and it can be very influent on the stability lobes.…”

Section: Introductionmentioning

confidence: 99%

Chatter avoidance in the milling of thin floors with bull-nose end mills: Model and stability diagrams

Campa

Lacalle

Celaya

2011

International Journal of Machine Tools and Manufacture

122

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The milling of thin parts is a high added value operation where the machinist has to face the chatter problem. The study of the stability of these operations is a complex task due to the changing modal parameters as the part losses mass during the machining and the complex shape of the tools that are used. The present work proposes a methodology for chatter avoidance in the milling of flexible thin floors with a bull-nose end mill. First, a stability model for the milling of compliant systems in the tool axis direction with bullnose end mills is presented. The contribution is the averaging method used to be able to use a linear model to predict the stability of the operation. Then, the procedure for the calculation of stability diagrams for the milling of thin floors is presented. The method is based on the estimation of the modal parameters of the part and the corresponding stability lobes during the machining. As in thin floor milling the depth of cut is already defined by the floor thickness previous to milling, it is proposed the use of stability diagrams that relate the tool position along the tool-path with the spindle speed. Hence, the sequence of spindle speeds that the tool must have during the milling can be selected. Finally, this methodology has been validated by means of experimental tests.

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“…Consequently, there are certain factors that are not included in the analysis, such as rigidity of the spindle assembly and potential impact of elevated temperature of machine tool elements during milling, as well as factors related to the workpiece (e.g. damping properties arising from the machined component's mass, potential material flaws or the non-uniform diameter of the area of cut) [13,15].…”

Section: State Of the Artmentioning

confidence: 99%

Effect of technological parameters on vibration acceleration in milling and vibration prediction with artificial neural networks

Zagórski

Kulisz

2019

MATEC Web Conf.

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This paper reports on the study of vibration acceleration in milling and vibration prediction by means of artificial neural networks. The milling process, carried out on AZ91D magnesium alloy with a PCD milling cutter, was monitored to observe the extent to which the change of selected technological parameters (vc, fz, ap) affects vibration acceleration ax, ay and az. The experimental data have shown a significant impact of technological parameters on maximum and RMS vibration acceleration. The simulation works employed the artificial neural networks modelled with Statistica Neural Network software. Two types of neural networks were employed: MLP (Multi-Layered Perceptron) and RBF (Radial Basis Function).

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Providing stability maps for milling operations

Cited by 17 publications

References 8 publications

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Trochoidal Milling and Neural Networks Simulation of Magnesium Alloys

Chatter avoidance in the milling of thin floors with bull-nose end mills: Model and stability diagrams

Effect of technological parameters on vibration acceleration in milling and vibration prediction with artificial neural networks

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